Overview

Dataset statistics

Number of variables62
Number of observations20000
Missing cells61479
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 MiB
Average record size in memory496.0 B

Variable types

Categorical37
Numeric25

Alerts

n7 has constant value "0.0" Constant
n8 has constant value "0.0" Constant
n10 has constant value "0.0" Constant
alert_ids has a high cardinality: 20000 distinct values High cardinality
client_code has a high cardinality: 264 distinct values High cardinality
ip has a high cardinality: 8499 distinct values High cardinality
parent_category is highly correlated with ipcategory_name and 8 other fieldsHigh correlation
overallseverity is highly correlated with untrustscore and 2 other fieldsHigh correlation
timestamp_dist is highly correlated with correlatedcount and 2 other fieldsHigh correlation
correlatedcount is highly correlated with timestamp_dist and 4 other fieldsHigh correlation
n1 is highly correlated with n2 and 3 other fieldsHigh correlation
n2 is highly correlated with n1 and 3 other fieldsHigh correlation
n3 is highly correlated with n4 and 5 other fieldsHigh correlation
n4 is highly correlated with n3 and 6 other fieldsHigh correlation
n5 is highly correlated with n3 and 5 other fieldsHigh correlation
score is highly correlated with n1 and 12 other fieldsHigh correlation
srcip_cd is highly correlated with srcport_cd and 1 other fieldsHigh correlation
dstip_cd is highly correlated with timestamp_dist and 4 other fieldsHigh correlation
srcport_cd is highly correlated with correlatedcount and 1 other fieldsHigh correlation
dstport_cd is highly correlated with p9High correlation
alerttype_cd is highly correlated with n1 and 8 other fieldsHigh correlation
direction_cd is highly correlated with alerttype_cd and 10 other fieldsHigh correlation
eventname_cd is highly correlated with correlatedcount and 7 other fieldsHigh correlation
severity_cd is highly correlated with alerttype_cd and 9 other fieldsHigh correlation
reportingdevice_cd is highly correlated with eventname_cd and 2 other fieldsHigh correlation
devicetype_cd is highly correlated with alerttype_cd and 9 other fieldsHigh correlation
devicevendor_cd is highly correlated with alerttype_cd and 9 other fieldsHigh correlation
srcipcategory_cd is highly correlated with direction_cd and 6 other fieldsHigh correlation
dstipcategory_cd is highly correlated with direction_cd and 6 other fieldsHigh correlation
isiptrusted is highly correlated with ipcategory_name and 5 other fieldsHigh correlation
untrustscore is highly correlated with categoryname and 5 other fieldsHigh correlation
flowscore is highly correlated with alerttype_cd and 3 other fieldsHigh correlation
thrcnt_month is highly correlated with parent_category and 4 other fieldsHigh correlation
thrcnt_week is highly correlated with parent_category and 4 other fieldsHigh correlation
thrcnt_day is highly correlated with parent_category and 4 other fieldsHigh correlation
p6 is highly correlated with n1 and 8 other fieldsHigh correlation
p9 is highly correlated with timestamp_dist and 2 other fieldsHigh correlation
p5m is highly correlated with n3 and 5 other fieldsHigh correlation
p5w is highly correlated with n3 and 6 other fieldsHigh correlation
p5d is highly correlated with score and 3 other fieldsHigh correlation
p8m is highly correlated with n4 and 2 other fieldsHigh correlation
p8w is highly correlated with score and 2 other fieldsHigh correlation
p8d is highly correlated with n4 and 4 other fieldsHigh correlation
domain_cd is highly correlated with correlatedcount and 2 other fieldsHigh correlation
enforcementscore is highly correlated with overallseverityHigh correlation
n8 is highly correlated with enforcementscore and 32 other fieldsHigh correlation
grandparent_category is highly correlated with ipcategory_name and 4 other fieldsHigh correlation
dstportcategory_dominate is highly correlated with categoryname and 7 other fieldsHigh correlation
srcipcategory_dominate is highly correlated with ipcategory_name and 5 other fieldsHigh correlation
weekday is highly correlated with n8 and 2 other fieldsHigh correlation
n9 is highly correlated with scoreHigh correlation
trustscore is highly correlated with overallseverityHigh correlation
n6 is highly correlated with n3 and 2 other fieldsHigh correlation
dstipcategory_dominate is highly correlated with ipcategory_name and 4 other fieldsHigh correlation
srcportcategory_dominate is highly correlated with direction_cd and 6 other fieldsHigh correlation
categoryname is highly correlated with untrustscore and 1 other fieldsHigh correlation
n10 is highly correlated with enforcementscore and 32 other fieldsHigh correlation
ipcategory_name is highly correlated with ipcategory_scope and 5 other fieldsHigh correlation
ipcategory_scope is highly correlated with ipcategory_name and 5 other fieldsHigh correlation
n7 is highly correlated with enforcementscore and 32 other fieldsHigh correlation
username_cd is highly correlated with srcip_cdHigh correlation
n1 has 5589 (27.9%) missing values Missing
n2 has 5589 (27.9%) missing values Missing
n3 has 5589 (27.9%) missing values Missing
n4 has 5589 (27.9%) missing values Missing
n5 has 5589 (27.9%) missing values Missing
n6 has 5589 (27.9%) missing values Missing
n7 has 5589 (27.9%) missing values Missing
n8 has 5589 (27.9%) missing values Missing
n9 has 5589 (27.9%) missing values Missing
n10 has 5589 (27.9%) missing values Missing
score has 5589 (27.9%) missing values Missing
correlatedcount is highly skewed (γ1 = 84.65286314) Skewed
srcip_cd is highly skewed (γ1 = 20.88258636) Skewed
dstip_cd is highly skewed (γ1 = 77.10503233) Skewed
srcport_cd is highly skewed (γ1 = 34.06946492) Skewed
dstport_cd is highly skewed (γ1 = 21.11969578) Skewed
reportingdevice_cd is highly skewed (γ1 = 52.43333195) Skewed
domain_cd is highly skewed (γ1 = 141.4211824) Skewed
protocol_cd is highly skewed (γ1 = 73.68983205) Skewed
username_cd is highly skewed (γ1 = 55.59334911) Skewed
p9 is highly skewed (γ1 = 77.10866903) Skewed
alert_ids is uniformly distributed Uniform
alert_ids has unique values Unique
timestamp_dist has 6197 (31.0%) zeros Zeros
start_hour has 970 (4.9%) zeros Zeros
start_minute has 483 (2.4%) zeros Zeros
start_second has 314 (1.6%) zeros Zeros
score has 1962 (9.8%) zeros Zeros
srcip_cd has 594 (3.0%) zeros Zeros
dstip_cd has 1553 (7.8%) zeros Zeros
srcport_cd has 660 (3.3%) zeros Zeros
dstport_cd has 660 (3.3%) zeros Zeros
alerttype_cd has 594 (3.0%) zeros Zeros
direction_cd has 594 (3.0%) zeros Zeros
eventname_cd has 669 (3.3%) zeros Zeros
severity_cd has 594 (3.0%) zeros Zeros
reportingdevice_cd has 594 (3.0%) zeros Zeros
devicetype_cd has 660 (3.3%) zeros Zeros
domain_cd has 19093 (95.5%) zeros Zeros
protocol_cd has 9245 (46.2%) zeros Zeros
username_cd has 17531 (87.7%) zeros Zeros
p6 has 594 (3.0%) zeros Zeros
p9 has 19491 (97.5%) zeros Zeros

Reproduction

Analysis started2022-09-21 10:44:01.306285
Analysis finished2022-09-21 10:45:37.684996
Duration1 minute and 36.38 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

alert_ids
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Slg
 
1
oEI
 
1
PXy
 
1
YvK
 
1
YmS
 
1
Other values (19995)
19995 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters52
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20000 ?
Unique (%)100.0%

Sample

1st rowSlg
2nd rowWKM
3rd rowdkm
4th rowRIX
5th rowqFU

Common Values

ValueCountFrequency (%)
Slg1
 
< 0.1%
oEI1
 
< 0.1%
PXy1
 
< 0.1%
YvK1
 
< 0.1%
YmS1
 
< 0.1%
UqB1
 
< 0.1%
bkD1
 
< 0.1%
pvT1
 
< 0.1%
MxW1
 
< 0.1%
SoT1
 
< 0.1%
Other values (19990)19990
> 99.9%

Length

2022-09-21T12:45:37.756955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uaa6
 
< 0.1%
zzr6
 
< 0.1%
avb6
 
< 0.1%
qre6
 
< 0.1%
taf5
 
< 0.1%
hif5
 
< 0.1%
jtb5
 
< 0.1%
nfq5
 
< 0.1%
yzg5
 
< 0.1%
bcf5
 
< 0.1%
Other values (12437)19946
99.7%

Most occurring characters

ValueCountFrequency (%)
J1213
 
2.0%
M1208
 
2.0%
d1204
 
2.0%
E1189
 
2.0%
W1188
 
2.0%
F1184
 
2.0%
p1184
 
2.0%
c1184
 
2.0%
b1181
 
2.0%
U1179
 
2.0%
Other values (42)48086
80.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter30016
50.0%
Lowercase Letter29984
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J1213
 
4.0%
M1208
 
4.0%
E1189
 
4.0%
W1188
 
4.0%
F1184
 
3.9%
U1179
 
3.9%
I1174
 
3.9%
T1174
 
3.9%
B1169
 
3.9%
A1167
 
3.9%
Other values (16)18171
60.5%
Lowercase Letter
ValueCountFrequency (%)
d1204
 
4.0%
p1184
 
3.9%
c1184
 
3.9%
b1181
 
3.9%
x1177
 
3.9%
l1174
 
3.9%
y1174
 
3.9%
f1172
 
3.9%
t1172
 
3.9%
a1167
 
3.9%
Other values (16)18195
60.7%

Most occurring scripts

ValueCountFrequency (%)
Latin60000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J1213
 
2.0%
M1208
 
2.0%
d1204
 
2.0%
E1189
 
2.0%
W1188
 
2.0%
F1184
 
2.0%
p1184
 
2.0%
c1184
 
2.0%
b1181
 
2.0%
U1179
 
2.0%
Other values (42)48086
80.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
J1213
 
2.0%
M1208
 
2.0%
d1204
 
2.0%
E1189
 
2.0%
W1188
 
2.0%
F1184
 
2.0%
p1184
 
2.0%
c1184
 
2.0%
b1181
 
2.0%
U1179
 
2.0%
Other values (42)48086
80.1%

client_code
Categorical

HIGH CARDINALITY

Distinct264
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
OTS
 
419
WYF
 
412
UZT
 
367
QQH
 
341
MBG
 
332
Other values (259)
18129 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRLJ
2nd rowUZT
3rd rowZZW
4th rowQXG
5th rowPDU

Common Values

ValueCountFrequency (%)
OTS419
 
2.1%
WYF412
 
2.1%
UZT367
 
1.8%
QQH341
 
1.7%
MBG332
 
1.7%
PCV326
 
1.6%
AMC320
 
1.6%
OPC263
 
1.3%
OUW249
 
1.2%
SHB241
 
1.2%
Other values (254)16730
83.7%

Length

2022-09-21T12:45:37.855006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ots419
 
2.1%
wyf412
 
2.1%
uzt367
 
1.8%
qqh341
 
1.7%
mbg332
 
1.7%
pcv326
 
1.6%
amc320
 
1.6%
opc263
 
1.3%
ouw249
 
1.2%
shb241
 
1.2%
Other values (254)16730
83.7%

Most occurring characters

ValueCountFrequency (%)
Q3360
 
5.6%
B3159
 
5.3%
C3024
 
5.0%
W2917
 
4.9%
T2866
 
4.8%
U2736
 
4.6%
V2616
 
4.4%
Z2583
 
4.3%
R2546
 
4.2%
S2455
 
4.1%
Other values (16)31738
52.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter60000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Q3360
 
5.6%
B3159
 
5.3%
C3024
 
5.0%
W2917
 
4.9%
T2866
 
4.8%
U2736
 
4.6%
V2616
 
4.4%
Z2583
 
4.3%
R2546
 
4.2%
S2455
 
4.1%
Other values (16)31738
52.9%

Most occurring scripts

ValueCountFrequency (%)
Latin60000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q3360
 
5.6%
B3159
 
5.3%
C3024
 
5.0%
W2917
 
4.9%
T2866
 
4.8%
U2736
 
4.6%
V2616
 
4.4%
Z2583
 
4.3%
R2546
 
4.2%
S2455
 
4.1%
Other values (16)31738
52.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Q3360
 
5.6%
B3159
 
5.3%
C3024
 
5.0%
W2917
 
4.9%
T2866
 
4.8%
U2736
 
4.6%
V2616
 
4.4%
Z2583
 
4.3%
R2546
 
4.2%
S2455
 
4.1%
Other values (16)31738
52.9%

categoryname
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Exploit
9632 
Attack
7288 
Control and Maintain
1973 
Reconnaissance
 
517
Attack Preparation
 
390

Length

Max length20
Median length18
Mean length8.3435
Min length6

Characters and Unicode

Total characters166870
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExploit
2nd rowExploit
3rd rowAttack
4th rowAttack
5th rowExploit

Common Values

ValueCountFrequency (%)
Exploit9632
48.2%
Attack7288
36.4%
Control and Maintain1973
 
9.9%
Reconnaissance517
 
2.6%
Attack Preparation390
 
1.9%
Compromise200
 
1.0%

Length

2022-09-21T12:45:37.955018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:38.080425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
exploit9632
39.6%
attack7678
31.5%
control1973
 
8.1%
and1973
 
8.1%
maintain1973
 
8.1%
reconnaissance517
 
2.1%
preparation390
 
1.6%
compromise200
 
0.8%

Most occurring characters

ValueCountFrequency (%)
t29324
17.6%
a15411
9.2%
o14885
8.9%
i14685
8.8%
l11605
 
7.0%
p10222
 
6.1%
n9833
 
5.9%
E9632
 
5.8%
x9632
 
5.8%
c8712
 
5.2%
Other values (12)32929
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter140171
84.0%
Uppercase Letter22363
 
13.4%
Space Separator4336
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t29324
20.9%
a15411
11.0%
o14885
10.6%
i14685
10.5%
l11605
 
8.3%
p10222
 
7.3%
n9833
 
7.0%
x9632
 
6.9%
c8712
 
6.2%
k7678
 
5.5%
Other values (5)8184
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
E9632
43.1%
A7678
34.3%
C2173
 
9.7%
M1973
 
8.8%
R517
 
2.3%
P390
 
1.7%
Space Separator
ValueCountFrequency (%)
4336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin162534
97.4%
Common4336
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t29324
18.0%
a15411
9.5%
o14885
9.2%
i14685
9.0%
l11605
 
7.1%
p10222
 
6.3%
n9833
 
6.0%
E9632
 
5.9%
x9632
 
5.9%
c8712
 
5.4%
Other values (11)28593
17.6%
Common
ValueCountFrequency (%)
4336
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII166870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t29324
17.6%
a15411
9.2%
o14885
8.9%
i14685
8.8%
l11605
 
7.0%
p10222
 
6.1%
n9833
 
5.9%
E9632
 
5.8%
x9632
 
5.8%
c8712
 
5.2%
Other values (12)32929
19.7%

ip
Categorical

HIGH CARDINALITY

Distinct8499
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
YT.LB.32.21
 
628
MC.ER.197.27
 
518
YT.LB.34.21
 
434
YT.RD.254.202
 
413
OQ.QJ.38.32
 
373
Other values (8494)
17634 

Length

Max length13
Median length12
Mean length11.62725
Min length8

Characters and Unicode

Total characters232545
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6050 ?
Unique (%)30.2%

Sample

1st rowMW.YB.50.64
2nd rowIJ.NW.77.74
3rd rowYT.LB.36.21
4th row172.BW.LB.105
5th rowYT.LB.32.110

Common Values

ValueCountFrequency (%)
YT.LB.32.21628
 
3.1%
MC.ER.197.27518
 
2.6%
YT.LB.34.21434
 
2.2%
YT.RD.254.202413
 
2.1%
OQ.QJ.38.32373
 
1.9%
YT.LB.36.21352
 
1.8%
YT.LB.38.21340
 
1.7%
YT.LB.32.10319
 
1.6%
YT.LB.36.10191
 
1.0%
ZU.SK.99.55174
 
0.9%
Other values (8489)16258
81.3%

Length

2022-09-21T12:45:38.181772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
yt.lb.32.21628
 
3.1%
mc.er.197.27518
 
2.6%
yt.lb.34.21434
 
2.2%
yt.rd.254.202413
 
2.1%
oq.qj.38.32373
 
1.9%
yt.lb.36.21352
 
1.8%
yt.lb.38.21340
 
1.7%
yt.lb.32.10319
 
1.6%
yt.lb.36.10191
 
1.0%
zu.sk.99.55174
 
0.9%
Other values (8489)16258
81.3%

Most occurring characters

ValueCountFrequency (%)
.60000
25.8%
124425
 
10.5%
215729
 
6.8%
012015
 
5.2%
39057
 
3.9%
L6193
 
2.7%
96148
 
2.6%
B6038
 
2.6%
T5966
 
2.6%
75751
 
2.5%
Other values (27)81223
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number92577
39.8%
Uppercase Letter79968
34.4%
Other Punctuation60000
25.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L6193
 
7.7%
B6038
 
7.6%
T5966
 
7.5%
Y5614
 
7.0%
S4027
 
5.0%
J3640
 
4.6%
E3472
 
4.3%
O3373
 
4.2%
R3215
 
4.0%
F2597
 
3.2%
Other values (16)35833
44.8%
Decimal Number
ValueCountFrequency (%)
124425
26.4%
215729
17.0%
012015
13.0%
39057
 
9.8%
96148
 
6.6%
75751
 
6.2%
45446
 
5.9%
55414
 
5.8%
64589
 
5.0%
84003
 
4.3%
Other Punctuation
ValueCountFrequency (%)
.60000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common152577
65.6%
Latin79968
34.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
L6193
 
7.7%
B6038
 
7.6%
T5966
 
7.5%
Y5614
 
7.0%
S4027
 
5.0%
J3640
 
4.6%
E3472
 
4.3%
O3373
 
4.2%
R3215
 
4.0%
F2597
 
3.2%
Other values (16)35833
44.8%
Common
ValueCountFrequency (%)
.60000
39.3%
124425
16.0%
215729
 
10.3%
012015
 
7.9%
39057
 
5.9%
96148
 
4.0%
75751
 
3.8%
45446
 
3.6%
55414
 
3.5%
64589
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII232545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.60000
25.8%
124425
 
10.5%
215729
 
6.8%
012015
 
5.2%
39057
 
3.9%
L6193
 
2.7%
96148
 
2.6%
B6038
 
2.6%
T5966
 
2.6%
75751
 
2.5%
Other values (27)81223
34.9%

ipcategory_name
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
INTERNET
11005 
PRIV-10
6107 
PRIV-192
1647 
PRIV-172
1225 
LOOPBACK
 
6
Other values (4)
 
10

Length

Max length10
Median length8
Mean length7.69515
Min length5

Characters and Unicode

Total characters153903
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowINTERNET
2nd rowINTERNET
3rd rowINTERNET
4th rowPRIV-172
5th rowINTERNET

Common Values

ValueCountFrequency (%)
INTERNET11005
55.0%
PRIV-106107
30.5%
PRIV-1921647
 
8.2%
PRIV-1721225
 
6.1%
LOOPBACK6
 
< 0.1%
LINK-LOCAL4
 
< 0.1%
MULTICAST3
 
< 0.1%
BROADCAST2
 
< 0.1%
BENCH1
 
< 0.1%

Length

2022-09-21T12:45:38.274565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:38.403330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
internet11005
55.0%
priv-106107
30.5%
priv-1921647
 
8.2%
priv-1721225
 
6.1%
loopback6
 
< 0.1%
link-local4
 
< 0.1%
multicast3
 
< 0.1%
broadcast2
 
< 0.1%
bench1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T22018
14.3%
N22015
14.3%
E22011
14.3%
I19991
13.0%
R19986
13.0%
P8985
5.8%
-8983
5.8%
V8979
5.8%
18979
5.8%
06107
 
4.0%
Other values (14)5849
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter124090
80.6%
Decimal Number20830
 
13.5%
Dash Punctuation8983
 
5.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T22018
17.7%
N22015
17.7%
E22011
17.7%
I19991
16.1%
R19986
16.1%
P8985
7.2%
V8979
7.2%
L21
 
< 0.1%
O18
 
< 0.1%
A17
 
< 0.1%
Other values (8)49
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
18979
43.1%
06107
29.3%
22872
 
13.8%
91647
 
7.9%
71225
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
-8983
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin124090
80.6%
Common29813
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T22018
17.7%
N22015
17.7%
E22011
17.7%
I19991
16.1%
R19986
16.1%
P8985
7.2%
V8979
7.2%
L21
 
< 0.1%
O18
 
< 0.1%
A17
 
< 0.1%
Other values (8)49
 
< 0.1%
Common
ValueCountFrequency (%)
-8983
30.1%
18979
30.1%
06107
20.5%
22872
 
9.6%
91647
 
5.5%
71225
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII153903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T22018
14.3%
N22015
14.3%
E22011
14.3%
I19991
13.0%
R19986
13.0%
P8985
5.8%
-8983
5.8%
V8979
5.8%
18979
5.8%
06107
 
4.0%
Other values (14)5849
 
3.8%

ipcategory_scope
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Internet
11008 
Private network
8980 
Subnet
 
6
Host
 
6

Length

Max length15
Median length8
Mean length11.1412
Min length4

Characters and Unicode

Total characters222824
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInternet
2nd rowInternet
3rd rowInternet
4th rowPrivate network
5th rowInternet

Common Values

ValueCountFrequency (%)
Internet11008
55.0%
Private network8980
44.9%
Subnet6
 
< 0.1%
Host6
 
< 0.1%

Length

2022-09-21T12:45:38.529619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:38.636914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
internet11008
38.0%
private8980
31.0%
network8980
31.0%
subnet6
 
< 0.1%
host6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t39988
17.9%
e39982
17.9%
n31002
13.9%
r28968
13.0%
I11008
 
4.9%
o8986
 
4.0%
k8980
 
4.0%
w8980
 
4.0%
8980
 
4.0%
a8980
 
4.0%
Other values (8)26970
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter193844
87.0%
Uppercase Letter20000
 
9.0%
Space Separator8980
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t39988
20.6%
e39982
20.6%
n31002
16.0%
r28968
14.9%
o8986
 
4.6%
k8980
 
4.6%
w8980
 
4.6%
a8980
 
4.6%
v8980
 
4.6%
i8980
 
4.6%
Other values (3)18
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
I11008
55.0%
P8980
44.9%
S6
 
< 0.1%
H6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
8980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin213844
96.0%
Common8980
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t39988
18.7%
e39982
18.7%
n31002
14.5%
r28968
13.5%
I11008
 
5.1%
o8986
 
4.2%
k8980
 
4.2%
w8980
 
4.2%
a8980
 
4.2%
v8980
 
4.2%
Other values (7)17990
8.4%
Common
ValueCountFrequency (%)
8980
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII222824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t39988
17.9%
e39982
17.9%
n31002
13.9%
r28968
13.0%
I11008
 
4.9%
o8986
 
4.0%
k8980
 
4.0%
w8980
 
4.0%
8980
 
4.0%
a8980
 
4.0%
Other values (8)26970
12.1%

parent_category
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
7
11005 
1
8979 
4
 
8
3
 
5
5
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row1
5th row7

Common Values

ValueCountFrequency (%)
711005
55.0%
18979
44.9%
48
 
< 0.1%
35
 
< 0.1%
53
 
< 0.1%

Length

2022-09-21T12:45:38.741820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:38.843458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
711005
55.0%
18979
44.9%
48
 
< 0.1%
35
 
< 0.1%
53
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
711005
55.0%
18979
44.9%
48
 
< 0.1%
35
 
< 0.1%
53
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
711005
55.0%
18979
44.9%
48
 
< 0.1%
35
 
< 0.1%
53
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
711005
55.0%
18979
44.9%
48
 
< 0.1%
35
 
< 0.1%
53
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
711005
55.0%
18979
44.9%
48
 
< 0.1%
35
 
< 0.1%
53
 
< 0.1%

grandparent_category
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
A
19989 
B
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A19989
99.9%
B11
 
0.1%

Length

2022-09-21T12:45:38.934107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:39.028775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
a19989
99.9%
b11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A19989
99.9%
B11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter20000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A19989
99.9%
B11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin20000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A19989
99.9%
B11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A19989
99.9%
B11
 
0.1%

overallseverity
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
3
8880 
5
4246 
4
3757 
2
3008 
1
 
109

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
38880
44.4%
54246
21.2%
43757
18.8%
23008
 
15.0%
1109
 
0.5%

Length

2022-09-21T12:45:39.105781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:39.210593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
38880
44.4%
54246
21.2%
43757
18.8%
23008
 
15.0%
1109
 
0.5%

Most occurring characters

ValueCountFrequency (%)
38880
44.4%
54246
21.2%
43757
18.8%
23008
 
15.0%
1109
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
38880
44.4%
54246
21.2%
43757
18.8%
23008
 
15.0%
1109
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
38880
44.4%
54246
21.2%
43757
18.8%
23008
 
15.0%
1109
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38880
44.4%
54246
21.2%
43757
18.8%
23008
 
15.0%
1109
 
0.5%

timestamp_dist
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9622
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35821.62125
Minimum0
Maximum1391963
Zeros6197
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:39.320628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1808
Q317072
95-th percentile229707
Maximum1391963
Range1391963
Interquartile range (IQR)17072

Descriptive statistics

Standard deviation99712.09662
Coefficient of variation (CV)2.783572969
Kurtosis30.79741658
Mean35821.62125
Median Absolute Deviation (MAD)1808
Skewness4.858193628
Sum716432425
Variance9942502212
MonotonicityNot monotonic
2022-09-21T12:45:39.439256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06197
31.0%
1174
 
0.9%
292
 
0.5%
685
 
0.4%
769
 
0.3%
2066
 
0.3%
462
 
0.3%
362
 
0.3%
556
 
0.3%
1053
 
0.3%
Other values (9612)13084
65.4%
ValueCountFrequency (%)
06197
31.0%
1174
 
0.9%
292
 
0.5%
362
 
0.3%
462
 
0.3%
556
 
0.3%
685
 
0.4%
769
 
0.3%
845
 
0.2%
953
 
0.3%
ValueCountFrequency (%)
13919631
< 0.1%
12239951
< 0.1%
10349591
< 0.1%
10233201
< 0.1%
10165131
< 0.1%
10135691
< 0.1%
10123381
< 0.1%
10123281
< 0.1%
9867641
< 0.1%
9778601
< 0.1%

start_hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.47125
Minimum0
Maximum23
Zeros970
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:39.552047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median13
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.239985795
Coefficient of variation (CV)0.580534092
Kurtosis-1.237834059
Mean12.47125
Median Absolute Deviation (MAD)6
Skewness-0.2415790605
Sum249425
Variance52.41739431
MonotonicityNot monotonic
2022-09-21T12:45:39.643177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201221
 
6.1%
231132
 
5.7%
181074
 
5.4%
171052
 
5.3%
221029
 
5.1%
211001
 
5.0%
0970
 
4.9%
19963
 
4.8%
1904
 
4.5%
15894
 
4.5%
Other values (14)9760
48.8%
ValueCountFrequency (%)
0970
4.9%
1904
4.5%
2837
4.2%
3724
3.6%
4545
2.7%
5622
3.1%
6649
3.2%
7617
3.1%
8690
3.5%
9613
3.1%
ValueCountFrequency (%)
231132
5.7%
221029
5.1%
211001
5.0%
201221
6.1%
19963
4.8%
181074
5.4%
171052
5.3%
16890
4.5%
15894
4.5%
14711
3.6%

start_minute
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.0901
Minimum0
Maximum59
Zeros483
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:39.752334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median29
Q344
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.56404016
Coefficient of variation (CV)0.6037806732
Kurtosis-1.218322161
Mean29.0901
Median Absolute Deviation (MAD)15
Skewness0.01568922978
Sum581802
Variance308.4955068
MonotonicityNot monotonic
2022-09-21T12:45:39.983681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0483
 
2.4%
2376
 
1.9%
12372
 
1.9%
59370
 
1.8%
3370
 
1.8%
21362
 
1.8%
55362
 
1.8%
50362
 
1.8%
33359
 
1.8%
7354
 
1.8%
Other values (50)16230
81.2%
ValueCountFrequency (%)
0483
2.4%
1326
1.6%
2376
1.9%
3370
1.8%
4348
1.7%
5344
1.7%
6335
1.7%
7354
1.8%
8305
1.5%
9301
1.5%
ValueCountFrequency (%)
59370
1.8%
58317
1.6%
57327
1.6%
56309
1.5%
55362
1.8%
54323
1.6%
53317
1.6%
52308
1.5%
51338
1.7%
50362
1.8%

start_second
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.14675
Minimum0
Maximum59
Zeros314
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:40.108590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median29
Q344
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.48471047
Coefficient of variation (CV)0.5998854236
Kurtosis-1.219522786
Mean29.14675
Median Absolute Deviation (MAD)15
Skewness0.02472124634
Sum582935
Variance305.7151002
MonotonicityNot monotonic
2022-09-21T12:45:40.224226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2399
 
2.0%
3386
 
1.9%
26376
 
1.9%
24375
 
1.9%
7369
 
1.8%
5366
 
1.8%
57358
 
1.8%
1356
 
1.8%
35353
 
1.8%
32353
 
1.8%
Other values (50)16309
81.5%
ValueCountFrequency (%)
0314
1.6%
1356
1.8%
2399
2.0%
3386
1.9%
4347
1.7%
5366
1.8%
6338
1.7%
7369
1.8%
8343
1.7%
9350
1.8%
ValueCountFrequency (%)
59331
1.7%
58313
1.6%
57358
1.8%
56323
1.6%
55348
1.7%
54340
1.7%
53334
1.7%
52330
1.7%
51334
1.7%
50340
1.7%

weekday
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
Fri
3952 
Thu
3853 
Wed
2983 
Tue
2725 
Sat
2358 
Other values (2)
4129 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWed
2nd rowSat
3rd rowSat
4th rowThu
5th rowFri

Common Values

ValueCountFrequency (%)
Fri3952
19.8%
Thu3853
19.3%
Wed2983
14.9%
Tue2725
13.6%
Sat2358
11.8%
Mon2148
10.7%
Sun1981
9.9%

Length

2022-09-21T12:45:40.335857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:40.447981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
fri3952
19.8%
thu3853
19.3%
wed2983
14.9%
tue2725
13.6%
sat2358
11.8%
mon2148
10.7%
sun1981
9.9%

Most occurring characters

ValueCountFrequency (%)
u8559
14.3%
T6578
11.0%
e5708
9.5%
S4339
 
7.2%
n4129
 
6.9%
F3952
 
6.6%
r3952
 
6.6%
i3952
 
6.6%
h3853
 
6.4%
W2983
 
5.0%
Other values (5)11995
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40000
66.7%
Uppercase Letter20000
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u8559
21.4%
e5708
14.3%
n4129
10.3%
r3952
9.9%
i3952
9.9%
h3853
9.6%
d2983
 
7.5%
a2358
 
5.9%
t2358
 
5.9%
o2148
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
T6578
32.9%
S4339
21.7%
F3952
19.8%
W2983
14.9%
M2148
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin60000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u8559
14.3%
T6578
11.0%
e5708
9.5%
S4339
 
7.2%
n4129
 
6.9%
F3952
 
6.6%
r3952
 
6.6%
i3952
 
6.6%
h3853
 
6.4%
W2983
 
5.0%
Other values (5)11995
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u8559
14.3%
T6578
11.0%
e5708
9.5%
S4339
 
7.2%
n4129
 
6.9%
F3952
 
6.6%
r3952
 
6.6%
i3952
 
6.6%
h3853
 
6.4%
W2983
 
5.0%
Other values (5)11995
20.0%

correlatedcount
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct826
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.1647
Minimum1
Maximum292479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:40.581961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q39
95-th percentile160
Maximum292479
Range292478
Interquartile range (IQR)8

Descriptive statistics

Standard deviation2727.057695
Coefficient of variation (CV)25.68704753
Kurtosis8027.337103
Mean106.1647
Median Absolute Deviation (MAD)1
Skewness84.65286314
Sum2123294
Variance7436843.67
MonotonicityNot monotonic
2022-09-21T12:45:40.718748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18350
41.8%
23065
 
15.3%
31250
 
6.2%
4824
 
4.1%
5518
 
2.6%
6402
 
2.0%
7303
 
1.5%
8284
 
1.4%
12209
 
1.0%
13208
 
1.0%
Other values (816)4587
22.9%
ValueCountFrequency (%)
18350
41.8%
23065
 
15.3%
31250
 
6.2%
4824
 
4.1%
5518
 
2.6%
6402
 
2.0%
7303
 
1.5%
8284
 
1.4%
9199
 
1.0%
10200
 
1.0%
ValueCountFrequency (%)
2924791
< 0.1%
1834151
< 0.1%
1448991
< 0.1%
396811
< 0.1%
220061
< 0.1%
210681
< 0.1%
209001
< 0.1%
191241
< 0.1%
186451
< 0.1%
177461
< 0.1%

n1
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
0.0
13683 
1.0
 
728

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.013683
68.4%
1.0728
 
3.6%
(Missing)5589
27.9%

Length

2022-09-21T12:45:40.849177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:40.960700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.013683
94.9%
1.0728
 
5.1%

Most occurring characters

ValueCountFrequency (%)
028094
65.0%
.14411
33.3%
1728
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028094
97.5%
1728
 
2.5%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028094
65.0%
.14411
33.3%
1728
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028094
65.0%
.14411
33.3%
1728
 
1.7%

n2
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
0.0
14285 
1.0
 
126

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.014285
71.4%
1.0126
 
0.6%
(Missing)5589
 
27.9%

Length

2022-09-21T12:45:41.049786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:41.160132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.014285
99.1%
1.0126
 
0.9%

Most occurring characters

ValueCountFrequency (%)
028696
66.4%
.14411
33.3%
1126
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028696
99.6%
1126
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028696
66.4%
.14411
33.3%
1126
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028696
66.4%
.14411
33.3%
1126
 
0.3%

n3
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
0.0
9365 
1.0
5046 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09365
46.8%
1.05046
25.2%
(Missing)5589
27.9%

Length

2022-09-21T12:45:41.250051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:41.361870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.09365
65.0%
1.05046
35.0%

Most occurring characters

ValueCountFrequency (%)
023776
55.0%
.14411
33.3%
15046
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023776
82.5%
15046
 
17.5%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023776
55.0%
.14411
33.3%
15046
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023776
55.0%
.14411
33.3%
15046
 
11.7%

n4
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
0.0
11593 
1.0
2818 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.011593
58.0%
1.02818
 
14.1%
(Missing)5589
27.9%

Length

2022-09-21T12:45:41.459224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:41.569625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.011593
80.4%
1.02818
 
19.6%

Most occurring characters

ValueCountFrequency (%)
026004
60.1%
.14411
33.3%
12818
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026004
90.2%
12818
 
9.8%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026004
60.1%
.14411
33.3%
12818
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026004
60.1%
.14411
33.3%
12818
 
6.5%

n5
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
0.0
9200 
1.0
5211 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09200
46.0%
1.05211
26.1%
(Missing)5589
27.9%

Length

2022-09-21T12:45:41.662479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:41.773010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.09200
63.8%
1.05211
36.2%

Most occurring characters

ValueCountFrequency (%)
023611
54.6%
.14411
33.3%
15211
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023611
81.9%
15211
 
18.1%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023611
54.6%
.14411
33.3%
15211
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023611
54.6%
.14411
33.3%
15211
 
12.1%

n6
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
1.0
7654 
0.0
6757 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.07654
38.3%
0.06757
33.8%
(Missing)5589
27.9%

Length

2022-09-21T12:45:41.870390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:41.980680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.07654
53.1%
0.06757
46.9%

Most occurring characters

ValueCountFrequency (%)
021168
49.0%
.14411
33.3%
17654
 
17.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
021168
73.4%
17654
 
26.6%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
021168
49.0%
.14411
33.3%
17654
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
021168
49.0%
.14411
33.3%
17654
 
17.7%

n7
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
0.0
14411 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.014411
72.1%
(Missing)5589
 
27.9%

Length

2022-09-21T12:45:42.076040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:42.179981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.014411
100.0%

Most occurring characters

ValueCountFrequency (%)
028822
66.7%
.14411
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028822
100.0%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028822
66.7%
.14411
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028822
66.7%
.14411
33.3%

n8
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
0.0
14411 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.014411
72.1%
(Missing)5589
 
27.9%

Length

2022-09-21T12:45:42.268579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:42.373527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.014411
100.0%

Most occurring characters

ValueCountFrequency (%)
028822
66.7%
.14411
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028822
100.0%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028822
66.7%
.14411
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028822
66.7%
.14411
33.3%

n9
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
0.0
14162 
1.0
 
249

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.014162
70.8%
1.0249
 
1.2%
(Missing)5589
 
27.9%

Length

2022-09-21T12:45:42.460341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:42.569770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.014162
98.3%
1.0249
 
1.7%

Most occurring characters

ValueCountFrequency (%)
028573
66.1%
.14411
33.3%
1249
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028573
99.1%
1249
 
0.9%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028573
66.1%
.14411
33.3%
1249
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028573
66.1%
.14411
33.3%
1249
 
0.6%

n10
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing5589
Missing (%)27.9%
Memory size156.4 KiB
0.0
14411 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters43233
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.014411
72.1%
(Missing)5589
 
27.9%

Length

2022-09-21T12:45:42.828062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:42.926055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.014411
100.0%

Most occurring characters

ValueCountFrequency (%)
028822
66.7%
.14411
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28822
66.7%
Other Punctuation14411
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028822
100.0%
Other Punctuation
ValueCountFrequency (%)
.14411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common43233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028822
66.7%
.14411
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII43233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028822
66.7%
.14411
33.3%

score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct8
Distinct (%)0.1%
Missing5589
Missing (%)27.9%
Infinite0
Infinite (%)0.0%
Mean2.261328152
Minimum0
Maximum7
Zeros1962
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:42.999819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.501880343
Coefficient of variation (CV)0.6641585131
Kurtosis-0.9972586717
Mean2.261328152
Median Absolute Deviation (MAD)1
Skewness0.07507271764
Sum32588
Variance2.255644565
MonotonicityNot monotonic
2022-09-21T12:45:43.079270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
34371
21.9%
13914
19.6%
42471
12.4%
01962
 
9.8%
2978
 
4.9%
5576
 
2.9%
6132
 
0.7%
77
 
< 0.1%
(Missing)5589
27.9%
ValueCountFrequency (%)
01962
9.8%
13914
19.6%
2978
 
4.9%
34371
21.9%
42471
12.4%
5576
 
2.9%
6132
 
0.7%
77
 
< 0.1%
ValueCountFrequency (%)
77
 
< 0.1%
6132
 
0.7%
5576
 
2.9%
42471
12.4%
34371
21.9%
2978
 
4.9%
13914
19.6%
01962
9.8%

srcip_cd
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct342
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.38045
Minimum0
Maximum3287
Zeros594
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:43.190311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile16
Maximum3287
Range3287
Interquartile range (IQR)0

Descriptive statistics

Standard deviation93.17957732
Coefficient of variation (CV)8.97644874
Kurtosis540.9733017
Mean10.38045
Median Absolute Deviation (MAD)0
Skewness20.88258636
Sum207609
Variance8682.433629
MonotonicityNot monotonic
2022-09-21T12:45:43.310677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115087
75.4%
21546
 
7.7%
0594
 
3.0%
3538
 
2.7%
4315
 
1.6%
5213
 
1.1%
6136
 
0.7%
7115
 
0.6%
885
 
0.4%
1071
 
0.4%
Other values (332)1300
 
6.5%
ValueCountFrequency (%)
0594
 
3.0%
115087
75.4%
21546
 
7.7%
3538
 
2.7%
4315
 
1.6%
5213
 
1.1%
6136
 
0.7%
7115
 
0.6%
885
 
0.4%
963
 
0.3%
ValueCountFrequency (%)
32871
< 0.1%
29131
< 0.1%
28751
< 0.1%
28421
< 0.1%
27281
< 0.1%
26962
< 0.1%
26201
< 0.1%
26001
< 0.1%
25791
< 0.1%
25501
< 0.1%

dstip_cd
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct421
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.7921
Minimum0
Maximum39007
Zeros1553
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:43.437707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile10
Maximum39007
Range39007
Interquartile range (IQR)0

Descriptive statistics

Standard deviation362.6361688
Coefficient of variation (CV)18.32226842
Kurtosis7411.41225
Mean19.7921
Median Absolute Deviation (MAD)0
Skewness77.10503233
Sum395842
Variance131504.9909
MonotonicityNot monotonic
2022-09-21T12:45:43.559123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114170
70.9%
21721
 
8.6%
01553
 
7.8%
3602
 
3.0%
4398
 
2.0%
5196
 
1.0%
7110
 
0.5%
6106
 
0.5%
868
 
0.3%
951
 
0.3%
Other values (411)1025
 
5.1%
ValueCountFrequency (%)
01553
 
7.8%
114170
70.9%
21721
 
8.6%
3602
 
3.0%
4398
 
2.0%
5196
 
1.0%
6106
 
0.5%
7110
 
0.5%
868
 
0.3%
951
 
0.3%
ValueCountFrequency (%)
390071
< 0.1%
197371
< 0.1%
177251
< 0.1%
52581
< 0.1%
49581
< 0.1%
49091
< 0.1%
43221
< 0.1%
42501
< 0.1%
34911
< 0.1%
27321
< 0.1%

srcport_cd
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct682
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.66705
Minimum0
Maximum27764
Zeros660
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:43.686295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q36
95-th percentile113
Maximum27764
Range27764
Interquartile range (IQR)5

Descriptive statistics

Standard deviation378.0522428
Coefficient of variation (CV)8.657608947
Kurtosis1870.924494
Mean43.66705
Median Absolute Deviation (MAD)1
Skewness34.06946492
Sum873341
Variance142923.4983
MonotonicityNot monotonic
2022-09-21T12:45:43.797755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19503
47.5%
22545
 
12.7%
31068
 
5.3%
4693
 
3.5%
0660
 
3.3%
5436
 
2.2%
6341
 
1.7%
7267
 
1.3%
8228
 
1.1%
13193
 
1.0%
Other values (672)4066
20.3%
ValueCountFrequency (%)
0660
 
3.3%
19503
47.5%
22545
 
12.7%
31068
 
5.3%
4693
 
3.5%
5436
 
2.2%
6341
 
1.7%
7267
 
1.3%
8228
 
1.1%
9179
 
0.9%
ValueCountFrequency (%)
277641
< 0.1%
171221
< 0.1%
133931
< 0.1%
117041
< 0.1%
103731
< 0.1%
73561
< 0.1%
73391
< 0.1%
72071
< 0.1%
71021
< 0.1%
69411
< 0.1%

dstport_cd
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct408
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.7341
Minimum0
Maximum9369
Zeros660
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:43.918419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile7
Maximum9369
Range9369
Interquartile range (IQR)0

Descriptive statistics

Standard deviation227.0743923
Coefficient of variation (CV)10.95173614
Kurtosis572.6309298
Mean20.7341
Median Absolute Deviation (MAD)0
Skewness21.11969578
Sum414682
Variance51562.77964
MonotonicityNot monotonic
2022-09-21T12:45:44.033906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115217
76.1%
22296
 
11.5%
0660
 
3.3%
3400
 
2.0%
4183
 
0.9%
5123
 
0.6%
684
 
0.4%
771
 
0.4%
1045
 
0.2%
843
 
0.2%
Other values (398)878
 
4.4%
ValueCountFrequency (%)
0660
 
3.3%
115217
76.1%
22296
 
11.5%
3400
 
2.0%
4183
 
0.9%
5123
 
0.6%
684
 
0.4%
771
 
0.4%
843
 
0.2%
943
 
0.2%
ValueCountFrequency (%)
93691
< 0.1%
89401
< 0.1%
74341
< 0.1%
66931
< 0.1%
57341
< 0.1%
55821
< 0.1%
50661
< 0.1%
47771
< 0.1%
47231
< 0.1%
47131
< 0.1%

alerttype_cd
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.17105
Minimum0
Maximum12
Zeros594
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:44.129146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5804384058
Coefficient of variation (CV)0.4956563817
Kurtosis23.71556521
Mean1.17105
Median Absolute Deviation (MAD)0
Skewness3.17948048
Sum23421
Variance0.3369087429
MonotonicityNot monotonic
2022-09-21T12:45:44.215119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
116371
81.9%
22282
 
11.4%
0594
 
3.0%
3593
 
3.0%
4123
 
0.6%
525
 
0.1%
67
 
< 0.1%
112
 
< 0.1%
72
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
0594
 
3.0%
116371
81.9%
22282
 
11.4%
3593
 
3.0%
4123
 
0.6%
525
 
0.1%
67
 
< 0.1%
72
 
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
121
 
< 0.1%
112
 
< 0.1%
72
 
< 0.1%
67
 
< 0.1%
525
 
0.1%
4123
 
0.6%
3593
 
3.0%
22282
 
11.4%
116371
81.9%
0594
 
3.0%

direction_cd
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0154
Minimum0
Maximum6
Zeros594
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:44.295102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2783284271
Coefficient of variation (CV)0.2741071766
Kurtosis19.36884214
Mean1.0154
Median Absolute Deviation (MAD)0
Skewness1.202227449
Sum20308
Variance0.07746671334
MonotonicityNot monotonic
2022-09-21T12:45:44.366221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
118521
92.6%
2875
 
4.4%
0594
 
3.0%
37
 
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
0594
 
3.0%
118521
92.6%
2875
 
4.4%
37
 
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
52
 
< 0.1%
37
 
< 0.1%
2875
 
4.4%
118521
92.6%
0594
 
3.0%

eventname_cd
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1826
Minimum0
Maximum14
Zeros669
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:44.443161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5837587504
Coefficient of variation (CV)0.4936231612
Kurtosis24.0689697
Mean1.1826
Median Absolute Deviation (MAD)0
Skewness2.997206689
Sum23652
Variance0.3407742787
MonotonicityNot monotonic
2022-09-21T12:45:44.518180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
115860
79.3%
22853
 
14.3%
0669
 
3.3%
3470
 
2.4%
496
 
0.5%
537
 
0.2%
68
 
< 0.1%
83
 
< 0.1%
73
 
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
0669
 
3.3%
115860
79.3%
22853
 
14.3%
3470
 
2.4%
496
 
0.5%
537
 
0.2%
68
 
< 0.1%
73
 
< 0.1%
83
 
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
83
 
< 0.1%
73
 
< 0.1%
68
 
< 0.1%
537
 
0.2%
496
 
0.5%
3470
 
2.4%
22853
 
14.3%
115860
79.3%
0669
 
3.3%

severity_cd
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2572
Minimum0
Maximum5
Zeros594
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:44.598765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8034802045
Coefficient of variation (CV)0.6391029307
Kurtosis9.404659987
Mean1.2572
Median Absolute Deviation (MAD)0
Skewness2.885431362
Sum25144
Variance0.645580439
MonotonicityNot monotonic
2022-09-21T12:45:44.675869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
116115
80.6%
21955
 
9.8%
3618
 
3.1%
0594
 
3.0%
5393
 
2.0%
4325
 
1.6%
ValueCountFrequency (%)
0594
 
3.0%
116115
80.6%
21955
 
9.8%
3618
 
3.1%
4325
 
1.6%
5393
 
2.0%
ValueCountFrequency (%)
5393
 
2.0%
4325
 
1.6%
3618
 
3.1%
21955
 
9.8%
116115
80.6%
0594
 
3.0%

reportingdevice_cd
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2025
Minimum0
Maximum144
Zeros594
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:44.764625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum144
Range144
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.455197454
Coefficient of variation (CV)1.210143413
Kurtosis4718.006345
Mean1.2025
Median Absolute Deviation (MAD)0
Skewness52.43333195
Sum24050
Variance2.11759963
MonotonicityNot monotonic
2022-09-21T12:45:44.861661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
116820
84.1%
21911
 
9.6%
0594
 
3.0%
3269
 
1.3%
4237
 
1.2%
549
 
0.2%
636
 
0.2%
724
 
0.1%
811
 
0.1%
106
 
< 0.1%
Other values (18)43
 
0.2%
ValueCountFrequency (%)
0594
 
3.0%
116820
84.1%
21911
 
9.6%
3269
 
1.3%
4237
 
1.2%
549
 
0.2%
636
 
0.2%
724
 
0.1%
811
 
0.1%
96
 
< 0.1%
ValueCountFrequency (%)
1441
 
< 0.1%
371
 
< 0.1%
311
 
< 0.1%
302
< 0.1%
281
 
< 0.1%
262
< 0.1%
251
 
< 0.1%
242
< 0.1%
212
< 0.1%
203
< 0.1%

devicetype_cd
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.024
Minimum0
Maximum7
Zeros660
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:45.067286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3092390361
Coefficient of variation (CV)0.3019912462
Kurtosis22.21267122
Mean1.024
Median Absolute Deviation (MAD)0
Skewness1.613288195
Sum20480
Variance0.09562878144
MonotonicityNot monotonic
2022-09-21T12:45:45.138225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
118240
91.2%
21070
 
5.3%
0660
 
3.3%
327
 
0.1%
62
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
0660
 
3.3%
118240
91.2%
21070
 
5.3%
327
 
0.1%
62
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
62
 
< 0.1%
327
 
0.1%
21070
 
5.3%
118240
91.2%
0660
 
3.3%

devicevendor_cd
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
18008 
2
 
1226
0
 
660
3
 
103
8
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
118008
90.0%
21226
 
6.1%
0660
 
3.3%
3103
 
0.5%
83
 
< 0.1%

Length

2022-09-21T12:45:45.225338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:45.319196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
118008
90.0%
21226
 
6.1%
0660
 
3.3%
3103
 
0.5%
83
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
118008
90.0%
21226
 
6.1%
0660
 
3.3%
3103
 
0.5%
83
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
118008
90.0%
21226
 
6.1%
0660
 
3.3%
3103
 
0.5%
83
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
118008
90.0%
21226
 
6.1%
0660
 
3.3%
3103
 
0.5%
83
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118008
90.0%
21226
 
6.1%
0660
 
3.3%
3103
 
0.5%
83
 
< 0.1%

domain_cd
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.9724
Minimum0
Maximum216836
Zeros19093
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:45.406674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum216836
Range216836
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1533.261765
Coefficient of variation (CV)139.7380486
Kurtosis19999.96722
Mean10.9724
Median Absolute Deviation (MAD)0
Skewness141.4211824
Sum219448
Variance2350891.639
MonotonicityNot monotonic
2022-09-21T12:45:45.504444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
019093
95.5%
1563
 
2.8%
2123
 
0.6%
350
 
0.2%
437
 
0.2%
529
 
0.1%
620
 
0.1%
718
 
0.1%
1112
 
0.1%
99
 
< 0.1%
Other values (19)46
 
0.2%
ValueCountFrequency (%)
019093
95.5%
1563
 
2.8%
2123
 
0.6%
350
 
0.2%
437
 
0.2%
529
 
0.1%
620
 
0.1%
718
 
0.1%
88
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
2168361
 
< 0.1%
1011
 
< 0.1%
681
 
< 0.1%
641
 
< 0.1%
351
 
< 0.1%
333
< 0.1%
321
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
233
< 0.1%

protocol_cd
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64405
Minimum0
Maximum131
Zeros9245
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:45.593107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum131
Range131
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.147133555
Coefficient of variation (CV)1.781124998
Kurtosis8339.63015
Mean0.64405
Median Absolute Deviation (MAD)1
Skewness73.68983205
Sum12881
Variance1.315915393
MonotonicityNot monotonic
2022-09-21T12:45:45.676143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
09245
46.2%
18945
44.7%
21677
 
8.4%
3105
 
0.5%
418
 
0.1%
55
 
< 0.1%
62
 
< 0.1%
121
 
< 0.1%
151
 
< 0.1%
1311
 
< 0.1%
ValueCountFrequency (%)
09245
46.2%
18945
44.7%
21677
 
8.4%
3105
 
0.5%
418
 
0.1%
55
 
< 0.1%
62
 
< 0.1%
121
 
< 0.1%
151
 
< 0.1%
1311
 
< 0.1%
ValueCountFrequency (%)
1311
 
< 0.1%
151
 
< 0.1%
121
 
< 0.1%
62
 
< 0.1%
55
 
< 0.1%
418
 
0.1%
3105
 
0.5%
21677
 
8.4%
18945
44.7%
09245
46.2%

username_cd
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3914
Minimum0
Maximum608
Zeros17531
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:45.780467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum608
Range608
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.113799009
Coefficient of variation (CV)18.17526574
Kurtosis3746.299611
Mean0.3914
Median Absolute Deviation (MAD)0
Skewness55.59334911
Sum7828
Variance50.60613635
MonotonicityNot monotonic
2022-09-21T12:45:45.895214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017531
87.7%
12089
 
10.4%
2147
 
0.7%
434
 
0.2%
334
 
0.2%
617
 
0.1%
515
 
0.1%
1111
 
0.1%
810
 
0.1%
2110
 
0.1%
Other values (42)102
 
0.5%
ValueCountFrequency (%)
017531
87.7%
12089
 
10.4%
2147
 
0.7%
334
 
0.2%
434
 
0.2%
515
 
0.1%
617
 
0.1%
79
 
< 0.1%
810
 
0.1%
99
 
< 0.1%
ValueCountFrequency (%)
6081
< 0.1%
3661
< 0.1%
3581
< 0.1%
3291
< 0.1%
2711
< 0.1%
2061
< 0.1%
1941
< 0.1%
1641
< 0.1%
1231
< 0.1%
1161
< 0.1%

srcipcategory_cd
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
17872 
2
 
1246
0
 
594
3
 
274
4
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
117872
89.4%
21246
 
6.2%
0594
 
3.0%
3274
 
1.4%
414
 
0.1%

Length

2022-09-21T12:45:46.003396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:46.097556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
117872
89.4%
21246
 
6.2%
0594
 
3.0%
3274
 
1.4%
414
 
0.1%

Most occurring characters

ValueCountFrequency (%)
117872
89.4%
21246
 
6.2%
0594
 
3.0%
3274
 
1.4%
414
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
117872
89.4%
21246
 
6.2%
0594
 
3.0%
3274
 
1.4%
414
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
117872
89.4%
21246
 
6.2%
0594
 
3.0%
3274
 
1.4%
414
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
117872
89.4%
21246
 
6.2%
0594
 
3.0%
3274
 
1.4%
414
 
0.1%

dstipcategory_cd
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
18832 
0
 
594
2
 
561
3
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
118832
94.2%
0594
 
3.0%
2561
 
2.8%
313
 
0.1%

Length

2022-09-21T12:45:46.181303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:46.275881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
118832
94.2%
0594
 
3.0%
2561
 
2.8%
313
 
0.1%

Most occurring characters

ValueCountFrequency (%)
118832
94.2%
0594
 
3.0%
2561
 
2.8%
313
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
118832
94.2%
0594
 
3.0%
2561
 
2.8%
313
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
118832
94.2%
0594
 
3.0%
2561
 
2.8%
313
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118832
94.2%
0594
 
3.0%
2561
 
2.8%
313
 
0.1%

isiptrusted
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
10499 
0
9501 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
110499
52.5%
09501
47.5%

Length

2022-09-21T12:45:46.356554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:46.445373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
110499
52.5%
09501
47.5%

Most occurring characters

ValueCountFrequency (%)
110499
52.5%
09501
47.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110499
52.5%
09501
47.5%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110499
52.5%
09501
47.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110499
52.5%
09501
47.5%

untrustscore
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.83125
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:46.513134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.600656597
Coefficient of variation (CV)0.5653533236
Kurtosis-1.495124306
Mean2.83125
Median Absolute Deviation (MAD)2
Skewness0.1983339679
Sum56625
Variance2.562101543
MonotonicityNot monotonic
2022-09-21T12:45:46.585517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
16429
32.1%
55273
26.4%
33391
17.0%
22992
15.0%
41912
 
9.6%
92
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
16429
32.1%
22992
15.0%
33391
17.0%
41912
 
9.6%
55273
26.4%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
81
 
< 0.1%
55273
26.4%
41912
 
9.6%
33391
17.0%
22992
15.0%
16429
32.1%

flowscore
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
3
16565 
4
2059 
5
 
1376

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
316565
82.8%
42059
 
10.3%
51376
 
6.9%

Length

2022-09-21T12:45:46.674986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:46.764783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
316565
82.8%
42059
 
10.3%
51376
 
6.9%

Most occurring characters

ValueCountFrequency (%)
316565
82.8%
42059
 
10.3%
51376
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
316565
82.8%
42059
 
10.3%
51376
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
316565
82.8%
42059
 
10.3%
51376
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
316565
82.8%
42059
 
10.3%
51376
 
6.9%

trustscore
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
3
18538 
5
 
1134
4
 
206
1
 
112
2
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
318538
92.7%
51134
 
5.7%
4206
 
1.0%
1112
 
0.6%
210
 
0.1%

Length

2022-09-21T12:45:46.845082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:46.941662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
318538
92.7%
51134
 
5.7%
4206
 
1.0%
1112
 
0.6%
210
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
318538
92.7%
51134
 
5.7%
4206
 
1.0%
1112
 
0.6%
210
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
318538
92.7%
51134
 
5.7%
4206
 
1.0%
1112
 
0.6%
210
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
318538
92.7%
51134
 
5.7%
4206
 
1.0%
1112
 
0.6%
210
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
318538
92.7%
51134
 
5.7%
4206
 
1.0%
1112
 
0.6%
210
 
< 0.1%

enforcementscore
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
0
11624 
-1
8376 

Length

Max length2
Median length1
Mean length1.4188
Min length1

Characters and Unicode

Total characters28376
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011624
58.1%
-18376
41.9%

Length

2022-09-21T12:45:47.026319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:47.114330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
011624
58.1%
18376
41.9%

Most occurring characters

ValueCountFrequency (%)
011624
41.0%
-8376
29.5%
18376
29.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
70.5%
Dash Punctuation8376
29.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011624
58.1%
18376
41.9%
Dash Punctuation
ValueCountFrequency (%)
-8376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011624
41.0%
-8376
29.5%
18376
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII28376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011624
41.0%
-8376
29.5%
18376
29.5%

dstipcategory_dominate
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
INTERNET
17570 
PRIV-10
1845 
PRIV-192
 
314
PRIV-172
 
222
PRIV-CGN
 
26
Other values (4)
 
23

Length

Max length10
Median length8
Mean length7.90875
Min length7

Characters and Unicode

Total characters158175
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINTERNET
2nd rowINTERNET
3rd rowINTERNET
4th rowINTERNET
5th rowINTERNET

Common Values

ValueCountFrequency (%)
INTERNET17570
87.8%
PRIV-101845
 
9.2%
PRIV-192314
 
1.6%
PRIV-172222
 
1.1%
PRIV-CGN26
 
0.1%
BROADCAST10
 
0.1%
MULTICAST6
 
< 0.1%
LOOPBACK5
 
< 0.1%
LINK-LOCAL2
 
< 0.1%

Length

2022-09-21T12:45:47.193708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:47.305224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
internet17570
87.8%
priv-101845
 
9.2%
priv-192314
 
1.6%
priv-172222
 
1.1%
priv-cgn26
 
0.1%
broadcast10
 
< 0.1%
multicast6
 
< 0.1%
loopback5
 
< 0.1%
link-local2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N35168
22.2%
T35162
22.2%
E35140
22.2%
R19987
12.6%
I19985
12.6%
P2412
 
1.5%
-2409
 
1.5%
V2407
 
1.5%
12381
 
1.5%
01845
 
1.2%
Other values (14)1279
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter150468
95.1%
Decimal Number5298
 
3.3%
Dash Punctuation2409
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N35168
23.4%
T35162
23.4%
E35140
23.4%
R19987
13.3%
I19985
13.3%
P2412
 
1.6%
V2407
 
1.6%
C49
 
< 0.1%
A33
 
< 0.1%
G26
 
< 0.1%
Other values (8)99
 
0.1%
Decimal Number
ValueCountFrequency (%)
12381
44.9%
01845
34.8%
2536
 
10.1%
9314
 
5.9%
7222
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
-2409
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin150468
95.1%
Common7707
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
N35168
23.4%
T35162
23.4%
E35140
23.4%
R19987
13.3%
I19985
13.3%
P2412
 
1.6%
V2407
 
1.6%
C49
 
< 0.1%
A33
 
< 0.1%
G26
 
< 0.1%
Other values (8)99
 
0.1%
Common
ValueCountFrequency (%)
-2409
31.3%
12381
30.9%
01845
23.9%
2536
 
7.0%
9314
 
4.1%
7222
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII158175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N35168
22.2%
T35162
22.2%
E35140
22.2%
R19987
12.6%
I19985
12.6%
P2412
 
1.5%
-2409
 
1.5%
V2407
 
1.5%
12381
 
1.5%
01845
 
1.2%
Other values (14)1279
 
0.8%

srcipcategory_dominate
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
PRIV-10
9763 
INTERNET
5292 
PRIV-192
2914 
PRIV-172
2020 
LINK-LOCAL
 
5
Other values (2)
 
6

Length

Max length10
Median length8
Mean length7.5122
Min length5

Characters and Unicode

Total characters150244
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPRIV-10
2nd rowINTERNET
3rd rowPRIV-10
4th rowPRIV-172
5th rowPRIV-192

Common Values

ValueCountFrequency (%)
PRIV-109763
48.8%
INTERNET5292
26.5%
PRIV-1922914
 
14.6%
PRIV-1722020
 
10.1%
LINK-LOCAL5
 
< 0.1%
LOOPBACK5
 
< 0.1%
BENCH1
 
< 0.1%

Length

2022-09-21T12:45:47.523899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:47.623867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
priv-109763
48.8%
internet5292
26.5%
priv-1922914
 
14.6%
priv-1722020
 
10.1%
link-local5
 
< 0.1%
loopback5
 
< 0.1%
bench1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I19994
13.3%
R19989
13.3%
P14702
9.8%
-14702
9.8%
V14697
9.8%
114697
9.8%
N10590
7.0%
E10585
7.0%
T10584
7.0%
09763
6.5%
Other values (10)9941
6.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter101214
67.4%
Decimal Number34328
 
22.8%
Dash Punctuation14702
 
9.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I19994
19.8%
R19989
19.7%
P14702
14.5%
V14697
14.5%
N10590
10.5%
E10585
10.5%
T10584
10.5%
L20
 
< 0.1%
O15
 
< 0.1%
C11
 
< 0.1%
Other values (4)27
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
114697
42.8%
09763
28.4%
24934
 
14.4%
92914
 
8.5%
72020
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
-14702
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin101214
67.4%
Common49030
32.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
I19994
19.8%
R19989
19.7%
P14702
14.5%
V14697
14.5%
N10590
10.5%
E10585
10.5%
T10584
10.5%
L20
 
< 0.1%
O15
 
< 0.1%
C11
 
< 0.1%
Other values (4)27
 
< 0.1%
Common
ValueCountFrequency (%)
-14702
30.0%
114697
30.0%
09763
19.9%
24934
 
10.1%
92914
 
5.9%
72020
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII150244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I19994
13.3%
R19989
13.3%
P14702
9.8%
-14702
9.8%
V14697
9.8%
114697
9.8%
N10590
7.0%
E10585
7.0%
T10584
7.0%
09763
6.5%
Other values (10)9941
6.6%

dstportcategory_dominate
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
2
15883 
3
1591 
1
 
1067
4
 
797
0
 
662

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
215883
79.4%
31591
 
8.0%
11067
 
5.3%
4797
 
4.0%
0662
 
3.3%

Length

2022-09-21T12:45:47.726535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:47.829410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
215883
79.4%
31591
 
8.0%
11067
 
5.3%
4797
 
4.0%
0662
 
3.3%

Most occurring characters

ValueCountFrequency (%)
215883
79.4%
31591
 
8.0%
11067
 
5.3%
4797
 
4.0%
0662
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
215883
79.4%
31591
 
8.0%
11067
 
5.3%
4797
 
4.0%
0662
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
215883
79.4%
31591
 
8.0%
11067
 
5.3%
4797
 
4.0%
0662
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
215883
79.4%
31591
 
8.0%
11067
 
5.3%
4797
 
4.0%
0662
 
3.3%

srcportcategory_dominate
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
4
11050 
3
5173 
2
1651 
1
1462 
0
 
664

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
411050
55.2%
35173
25.9%
21651
 
8.3%
11462
 
7.3%
0664
 
3.3%

Length

2022-09-21T12:45:47.922706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:48.034365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
411050
55.2%
35173
25.9%
21651
 
8.3%
11462
 
7.3%
0664
 
3.3%

Most occurring characters

ValueCountFrequency (%)
411050
55.2%
35173
25.9%
21651
 
8.3%
11462
 
7.3%
0664
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
411050
55.2%
35173
25.9%
21651
 
8.3%
11462
 
7.3%
0664
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
411050
55.2%
35173
25.9%
21651
 
8.3%
11462
 
7.3%
0664
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
411050
55.2%
35173
25.9%
21651
 
8.3%
11462
 
7.3%
0664
 
3.3%

thrcnt_month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3897
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2669.55525
Minimum1
Maximum18153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:48.160975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q135
median116
Q31221.25
95-th percentile16328.05
Maximum18153
Range18152
Interquartile range (IQR)1186.25

Descriptive statistics

Standard deviation5364.833747
Coefficient of variation (CV)2.00963578
Kurtosis2.242642391
Mean2669.55525
Median Absolute Deviation (MAD)107
Skewness1.974328245
Sum53391105
Variance28781441.13
MonotonicityNot monotonic
2022-09-21T12:45:48.308889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1511
 
2.6%
2338
 
1.7%
3258
 
1.3%
5229
 
1.1%
4219
 
1.1%
6206
 
1.0%
8186
 
0.9%
7186
 
0.9%
11161
 
0.8%
12154
 
0.8%
Other values (3887)17552
87.8%
ValueCountFrequency (%)
1511
2.6%
2338
1.7%
3258
1.3%
4219
1.1%
5229
1.1%
6206
1.0%
7186
 
0.9%
8186
 
0.9%
9134
 
0.7%
10142
 
0.7%
ValueCountFrequency (%)
181531
 
< 0.1%
181521
 
< 0.1%
181513
< 0.1%
181503
< 0.1%
181496
< 0.1%
181482
 
< 0.1%
181475
< 0.1%
181464
< 0.1%
181453
< 0.1%
181443
< 0.1%

thrcnt_week
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2114
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625.60425
Minimum1
Maximum4146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:48.465575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median33
Q3295.25
95-th percentile3792
Maximum4146
Range4145
Interquartile range (IQR)285.25

Descriptive statistics

Standard deviation1239.178175
Coefficient of variation (CV)1.980770071
Kurtosis2.114944196
Mean625.60425
Median Absolute Deviation (MAD)29
Skewness1.940972803
Sum12512085
Variance1535562.549
MonotonicityNot monotonic
2022-09-21T12:45:48.605226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11068
 
5.3%
2737
 
3.7%
3581
 
2.9%
4514
 
2.6%
5497
 
2.5%
6403
 
2.0%
7402
 
2.0%
8386
 
1.9%
9365
 
1.8%
10349
 
1.7%
Other values (2104)14698
73.5%
ValueCountFrequency (%)
11068
5.3%
2737
3.7%
3581
2.9%
4514
2.6%
5497
2.5%
6403
 
2.0%
7402
 
2.0%
8386
 
1.9%
9365
 
1.8%
10349
 
1.7%
ValueCountFrequency (%)
41461
 
< 0.1%
41451
 
< 0.1%
41441
 
< 0.1%
41422
 
< 0.1%
41413
< 0.1%
41403
< 0.1%
41393
< 0.1%
41385
< 0.1%
41371
 
< 0.1%
41362
 
< 0.1%

thrcnt_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct726
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.15465
Minimum1
Maximum866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:48.750161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q348
95-th percentile645.05
Maximum866
Range865
Interquartile range (IQR)46

Descriptive statistics

Standard deviation204.281704
Coefficient of variation (CV)2.039662702
Kurtosis2.923059189
Mean100.15465
Median Absolute Deviation (MAD)5
Skewness2.116137928
Sum2003093
Variance41731.01458
MonotonicityNot monotonic
2022-09-21T12:45:48.883673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13257
16.3%
22193
 
11.0%
31626
 
8.1%
41248
 
6.2%
5995
 
5.0%
6788
 
3.9%
7677
 
3.4%
8651
 
3.3%
9532
 
2.7%
10474
 
2.4%
Other values (716)7559
37.8%
ValueCountFrequency (%)
13257
16.3%
22193
11.0%
31626
8.1%
41248
 
6.2%
5995
 
5.0%
6788
 
3.9%
7677
 
3.4%
8651
 
3.3%
9532
 
2.7%
10474
 
2.4%
ValueCountFrequency (%)
8661
 
< 0.1%
8651
 
< 0.1%
8631
 
< 0.1%
8561
 
< 0.1%
8512
< 0.1%
8473
< 0.1%
8462
< 0.1%
8442
< 0.1%
8431
 
< 0.1%
8421
 
< 0.1%

p6
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.17105
Minimum0
Maximum12
Zeros594
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:48.994493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5804384058
Coefficient of variation (CV)0.4956563817
Kurtosis23.71556521
Mean1.17105
Median Absolute Deviation (MAD)0
Skewness3.17948048
Sum23421
Variance0.3369087429
MonotonicityNot monotonic
2022-09-21T12:45:49.097248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
116371
81.9%
22282
 
11.4%
0594
 
3.0%
3593
 
3.0%
4123
 
0.6%
525
 
0.1%
67
 
< 0.1%
112
 
< 0.1%
72
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
0594
 
3.0%
116371
81.9%
22282
 
11.4%
3593
 
3.0%
4123
 
0.6%
525
 
0.1%
67
 
< 0.1%
72
 
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
121
 
< 0.1%
112
 
< 0.1%
72
 
< 0.1%
67
 
< 0.1%
525
 
0.1%
4123
 
0.6%
3593
 
3.0%
22282
 
11.4%
116371
81.9%
0594
 
3.0%

p9
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct115
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.647
Minimum0
Maximum22000
Zeros19491
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2022-09-21T12:45:49.223769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum22000
Range22000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation200.305986
Coefficient of variation (CV)35.47122117
Kurtosis7673.593709
Mean5.647
Median Absolute Deviation (MAD)0
Skewness77.10866903
Sum112940
Variance40122.48802
MonotonicityNot monotonic
2022-09-21T12:45:49.360857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019491
97.5%
2103
 
0.5%
182
 
0.4%
430
 
0.1%
626
 
0.1%
323
 
0.1%
819
 
0.1%
1017
 
0.1%
716
 
0.1%
512
 
0.1%
Other values (105)181
 
0.9%
ValueCountFrequency (%)
019491
97.5%
182
 
0.4%
2103
 
0.5%
323
 
0.1%
430
 
0.1%
512
 
0.1%
626
 
0.1%
716
 
0.1%
819
 
0.1%
98
 
< 0.1%
ValueCountFrequency (%)
220001
< 0.1%
100961
< 0.1%
56811
< 0.1%
39971
< 0.1%
31881
< 0.1%
30931
< 0.1%
30901
< 0.1%
30721
< 0.1%
30641
< 0.1%
30401
< 0.1%

p5m
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
8919 
2
5683 
3
4606 
4
 
792

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
18919
44.6%
25683
28.4%
34606
23.0%
4792
 
4.0%

Length

2022-09-21T12:45:49.489074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:49.603237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
18919
44.6%
25683
28.4%
34606
23.0%
4792
 
4.0%

Most occurring characters

ValueCountFrequency (%)
18919
44.6%
25683
28.4%
34606
23.0%
4792
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
18919
44.6%
25683
28.4%
34606
23.0%
4792
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
18919
44.6%
25683
28.4%
34606
23.0%
4792
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18919
44.6%
25683
28.4%
34606
23.0%
4792
 
4.0%

p5w
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
13238 
2
4646 
3
1940 
4
 
176

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
113238
66.2%
24646
 
23.2%
31940
 
9.7%
4176
 
0.9%

Length

2022-09-21T12:45:49.706545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:49.822899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
113238
66.2%
24646
 
23.2%
31940
 
9.7%
4176
 
0.9%

Most occurring characters

ValueCountFrequency (%)
113238
66.2%
24646
 
23.2%
31940
 
9.7%
4176
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
113238
66.2%
24646
 
23.2%
31940
 
9.7%
4176
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
113238
66.2%
24646
 
23.2%
31940
 
9.7%
4176
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113238
66.2%
24646
 
23.2%
31940
 
9.7%
4176
 
0.9%

p5d
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
16153 
2
3133 
3
 
692
4
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116153
80.8%
23133
 
15.7%
3692
 
3.5%
422
 
0.1%

Length

2022-09-21T12:45:49.927947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:50.035330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
116153
80.8%
23133
 
15.7%
3692
 
3.5%
422
 
0.1%

Most occurring characters

ValueCountFrequency (%)
116153
80.8%
23133
 
15.7%
3692
 
3.5%
422
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116153
80.8%
23133
 
15.7%
3692
 
3.5%
422
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
116153
80.8%
23133
 
15.7%
3692
 
3.5%
422
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116153
80.8%
23133
 
15.7%
3692
 
3.5%
422
 
0.1%

p8m
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
14066 
2
3948 
3
1794 
4
 
166
5
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
114066
70.3%
23948
 
19.7%
31794
 
9.0%
4166
 
0.8%
526
 
0.1%

Length

2022-09-21T12:45:50.245765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:50.343752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
114066
70.3%
23948
 
19.7%
31794
 
9.0%
4166
 
0.8%
526
 
0.1%

Most occurring characters

ValueCountFrequency (%)
114066
70.3%
23948
 
19.7%
31794
 
9.0%
4166
 
0.8%
526
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
114066
70.3%
23948
 
19.7%
31794
 
9.0%
4166
 
0.8%
526
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
114066
70.3%
23948
 
19.7%
31794
 
9.0%
4166
 
0.8%
526
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
114066
70.3%
23948
 
19.7%
31794
 
9.0%
4166
 
0.8%
526
 
0.1%

p8w
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
16168 
2
3043 
3
 
724
4
 
59
5
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116168
80.8%
23043
 
15.2%
3724
 
3.6%
459
 
0.3%
56
 
< 0.1%

Length

2022-09-21T12:45:50.432331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:50.528975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
116168
80.8%
23043
 
15.2%
3724
 
3.6%
459
 
0.3%
56
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
116168
80.8%
23043
 
15.2%
3724
 
3.6%
459
 
0.3%
56
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116168
80.8%
23043
 
15.2%
3724
 
3.6%
459
 
0.3%
56
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
116168
80.8%
23043
 
15.2%
3724
 
3.6%
459
 
0.3%
56
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116168
80.8%
23043
 
15.2%
3724
 
3.6%
459
 
0.3%
56
 
< 0.1%

p8d
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1
18062 
2
 
1755
3
 
175
4
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
118062
90.3%
21755
 
8.8%
3175
 
0.9%
48
 
< 0.1%

Length

2022-09-21T12:45:50.618342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-21T12:45:50.712425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
118062
90.3%
21755
 
8.8%
3175
 
0.9%
48
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
118062
90.3%
21755
 
8.8%
3175
 
0.9%
48
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
118062
90.3%
21755
 
8.8%
3175
 
0.9%
48
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
118062
90.3%
21755
 
8.8%
3175
 
0.9%
48
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118062
90.3%
21755
 
8.8%
3175
 
0.9%
48
 
< 0.1%

Interactions

2022-09-21T12:45:32.874720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:21.187079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:24.233068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:27.091806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:30.221591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:33.025869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:36.293762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:39.174907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:42.042309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:45.322748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:48.123530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:51.249643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:54.149575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:57.132080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:00.237318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:03.062420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:06.350217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:09.176899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:12.125220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:15.161621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:17.956283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:21.328353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:24.276893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:27.120835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:30.041948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:32.994243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:21.341629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:24.342317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:27.229202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:30.335189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:33.150841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:36.400745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:39.286383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:42.311015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:45.433648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:48.232661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:51.372237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:54.260870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:57.267328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:00.348858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:03.181912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:06.459904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:09.285996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:12.267193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:15.271260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:18.072500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:21.448013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:24.382081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:27.260337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:30.150031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:33.102299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:21.462015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:24.440134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:27.350673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:30.438313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:33.259986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:36.500464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:39.386437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:42.432833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:45.648604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:48.334554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:51.479918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:54.361180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:57.393046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:00.452930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:03.292574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:06.563226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:09.384637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:12.387229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:15.369087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:18.182340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:21.557617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:24.477781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:27.518377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:30.253632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:33.218359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:21.581571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:24.548575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:27.481847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:30.545450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:33.372006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:36.607019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:39.496896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:42.566956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:45.750038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:48.441801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:51.594127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:54.467368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:57.520943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:00.567070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:03.416138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:06.670360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:09.486131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:12.519473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:15.475919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:18.327947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:21.674999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:24.579922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:27.641872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:30.359682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:33.337594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:21.701397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:24.653802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:27.624730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:30.654897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:33.483600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:36.714613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:39.605197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:42.701469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:45.855257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:48.702500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:51.709056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:54.580201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:57.647742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:00.674993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:03.541510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:06.777821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:09.587029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:12.660687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:15.587394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:18.491417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:21.788911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:24.683371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:27.762168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:30.470716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:33.464322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:21.823476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:24.757306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:27.768943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:30.768213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:33.598130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:36.826356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:39.709287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:42.851227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:45.960956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:48.819252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:51.825980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-21T12:44:56.637052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:59.816359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:02.624607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:05.922383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:08.757270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:11.638904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:14.753276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:17.439973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:20.777424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:23.752513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:26.635778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:29.653217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:32.454636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:35.646191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:23.899056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:26.737375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:29.907971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:32.719446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:35.827892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:38.760077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:41.668629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:44.997058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:47.816348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:50.934239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:53.842097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:56.763203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:59.922674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:02.732254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:06.032480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:08.861296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:11.760808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:14.865596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:17.652353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:20.889509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:23.856738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:26.759316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:29.753522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:32.552948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:35.746026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:24.009367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:26.856427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:30.010523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:32.816593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:36.073750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:38.862240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:41.790260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:45.104238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:47.919089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:51.038953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:53.943919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:56.886659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:00.024584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:02.837652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:06.137958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:08.965127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:11.879414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:14.963599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:17.751100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:21.112959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:23.959999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:26.878369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:29.850343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:32.656543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:35.851796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:24.124491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:26.977538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:30.118739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:32.918193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:36.191581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:39.080334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:41.919883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:45.215523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:48.026970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:51.147426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:54.049278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:44:57.016643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:00.130785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:02.953947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:06.246200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:09.073459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:12.003998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:15.065057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:17.855049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:21.223725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:24.178107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:27.000069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:29.948365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-21T12:45:32.768058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-21T12:45:50.855645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-21T12:45:51.555278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-21T12:45:52.256382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-21T12:45:52.962808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-21T12:45:53.264557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-21T12:45:36.278422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-21T12:45:37.142591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-21T12:45:37.381836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

alert_idsclient_codecategorynameipipcategory_nameipcategory_scopeparent_categorygrandparent_categoryoverallseveritytimestamp_diststart_hourstart_minutestart_secondweekdaycorrelatedcountn1n2n3n4n5n6n7n8n9n10scoresrcip_cddstip_cdsrcport_cddstport_cdalerttype_cddirection_cdeventname_cdseverity_cdreportingdevice_cddevicetype_cddevicevendor_cddomain_cdprotocol_cdusername_cdsrcipcategory_cddstipcategory_cdisiptrusteduntrustscoreflowscoretrustscoreenforcementscoredstipcategory_dominatesrcipcategory_dominatedstportcategory_dominatesrcportcategory_dominatethrcnt_monththrcnt_weekthrcnt_dayp6p9p5mp5wp5dp8mp8wp8d
0SlgRLJExploitMW.YB.50.64INTERNETInternet7A30114426Wed10.00.00.00.00.01.00.00.00.00.01.0111111111110101103330INTERNETPRIV-102413022984210111111
1WKMUZTExploitIJ.NW.77.74INTERNETInternet7A50223930Sat10.00.00.00.00.00.00.00.00.00.00.0111111111110101105330INTERNETINTERNET232011310111111
2dkmZZWAttackYT.LB.36.21INTERNETInternet7A307575Sat10.00.00.00.00.00.00.00.00.00.03.0111111111111101102330INTERNETPRIV-102416131360160210311111
3RIXQXGAttack172.BW.LB.105PRIV-172Private network1A3002133Thu10.00.01.01.01.01.00.00.00.00.04.0111111111111111112330INTERNETPRIV-172245312410311211
4qFUPDUExploitYT.LB.32.110INTERNETInternet7A3258273183929Fri140.00.00.00.00.01.00.00.00.00.01.01114111211110101103330INTERNETPRIV-192245411312010111111
5LKwZZEExploitXV.II.129.255INTERNETInternet7A5338814165548Fri13780.00.01.01.01.01.00.00.01.00.05.024511308321221110101104430INTERNETPRIV-1024623120321221
6lKTBUWExploitBY.XJ.15.50INTERNETInternet7A440489422595Fri500.00.00.00.00.00.00.00.00.00.03.02150111211110001103340INTERNETPRIV-102315078110221111
7pATKKLExploit10.KW.GO.5PRIV-10Private network1A2019531Tue1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11111111111000111133-1INTERNETPRIV-103311324410322322
8oyfOSCAttack10.AA.IU.62PRIV-10Private network1A42164645749Fri1051.01.01.01.01.01.00.00.00.00.06.015041331131110001113430INTERNETPRIV-10311842773102443332
9gGJAMCControl and MaintainSO.JE.235.122INTERNETInternet7A5482795341Fri751.00.01.01.01.01.00.00.00.00.05.0711734421244110201115430INTERNETINTERNET2315236420333222

Last rows

alert_idsclient_codecategorynameipipcategory_nameipcategory_scopeparent_categorygrandparent_categoryoverallseveritytimestamp_diststart_hourstart_minutestart_secondweekdaycorrelatedcountn1n2n3n4n5n6n7n8n9n10scoresrcip_cddstip_cdsrcport_cddstport_cdalerttype_cddirection_cdeventname_cdseverity_cdreportingdevice_cddevicetype_cddevicevendor_cddomain_cdprotocol_cdusername_cdsrcipcategory_cddstipcategory_cdisiptrusteduntrustscoreflowscoretrustscoreenforcementscoredstipcategory_dominatesrcipcategory_dominatedstportcategory_dominatesrcportcategory_dominatethrcnt_monththrcnt_weekthrcnt_dayp6p9p5mp5wp5dp8mp8wp8d
19990VvqIYBExploitUN.BR.118.18INTERNETInternet7A40235338Fri1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11111111111000110533-1PRIV-10INTERNET2441110111111
19991MqvFCQAttackMW.SW.158.234INTERNETInternet7A3011739Wed10.00.00.00.00.00.00.00.00.00.00.0111111111110001102330INTERNETPRIV-172329001822010111111
19992RJXJJXAttackWA.RN.18.184INTERNETInternet7A5467824926Mon10.00.01.00.01.01.00.00.00.00.03.0111111111110111105330INTERNETINTERNET244913110221111
19993nOQMDDAttack192.SL.WJ.109PRIV-192Private network1A3607518378Sun40.00.00.00.00.00.00.00.00.00.05.0134321121110001112330INTERNETPRIV-19224637321432222
19994MCKMNFExploitRD.GZ.91.228INTERNETInternet7A1010127Tue1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11111111111000110131-1INTERNETPRIV-102499910222111
19995rubZRVExploitYT.LB.133.99INTERNETInternet7A24320119127Sun30.00.00.00.00.00.00.00.00.00.01.011311111111000110133-1INTERNETPRIV-102329075410111111
19996NtyBMUExploitMA.LA.169.242INTERNETInternet7A3013126Thu10.00.01.01.01.01.00.00.00.00.04.0111111111110101103330INTERNETPRIV-102329539810221222
19997TmAPCVExploit10.NF.RC.138PRIV-10Private network1A5118448Sat20.00.00.00.00.00.00.00.00.00.01.0111111212220211115330PRIV-10INTERNET2371110111111
19998zkvJITAttackKN.IR.6.26INTERNETInternet7A374884738Mon10.00.01.01.01.01.00.00.01.00.05.011111111111000110433-1INTERNETPRIV-10244015110321321
19999ADaOUWExploitJM.KV.177.114INTERNETInternet7A51311113174Fri20NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN17181721221110001115430INTERNETINTERNET3221555620333322